What is GET3D?
We develop a three-dimensional signed distance field (SDF) alongside a textured field using two latent codes. To extract a 3D surface mesh from the SDF, we utilize DMTet, sampling the texture field at surface points for color information. Our training process includes adversarial losses centered on 2D images, employing a rasterization-based differentiable renderer to generate both RGB visuals and silhouettes. To differentiate between real and generated inputs, we introduce two distinct 2D discriminators—one dedicated to RGB images and the other to silhouettes. The entire system is structured to enable end-to-end training. As various industries shift towards creating expansive 3D virtual environments, the necessity for scalable tools capable of generating large volumes of high-quality and diverse 3D content becomes increasingly evident. Our research aims to develop robust 3D generative models that produce textured meshes, facilitating their seamless integration into 3D rendering engines for immediate deployment in a range of applications. This strategy not only addresses the challenge of scalability but also opens up new avenues for innovative uses in fields like virtual reality and gaming. Moreover, by enhancing the quality and diversity of 3D content, we aim to push the boundaries of creativity and interactivity within these immersive environments.
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Company Facts
Company Name:
NVIDIA
Company Location:
United States
Company Website:
nv-tlabs.github.io/GET3D/
Product Details
Deployment
SaaS
Training Options
Documentation Hub
Support
Web-Based Support
Product Details
Target Company Sizes
Individual
1-10
11-50
51-200
201-500
501-1000
1001-5000
5001-10000
10001+
Target Organization Types
Mid Size Business
Small Business
Enterprise
Freelance
Nonprofit
Government
Startup
Supported Languages
English